Lecture #2 How to describe a hybrid system? Formal models for hybrid system João P. Hespanha...
Transcript of Lecture #2 How to describe a hybrid system? Formal models for hybrid system João P. Hespanha...
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Lecture #2How to describe a hybrid system?Formal models for hybrid system
João P. Hespanha
University of Californiaat Santa Barbara
Hybrid Control and Switched Systems
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Summary
1. Formal models for hybrid systems:• Finite automata• Differential equations• Hybrid automata• Open hybrid automaton
2. Nondeterministic vs. stochastic systems• Non-deterministic hybrid automata • Stochastic hybrid automata
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Example #5: Multiple-tank system
y
pump goal ´ prevent the tank from emptying or filling up
constant outflow ´
pump-on inflow ´ ´ delay between command is sent to pump and the time it is executed
pump off
wait to on pump on
wait to off
y · ymin ?
y ¸ ymax ? › 0
¸ ?
¸ ?
› 0
How to formally describe this hybrid system?
guard condition
state reset
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Deterministic finite automaton
Q {q1, q2, …, qn} ´ finite set of states {a, b, c,… } ´ finite set of input symbols (alphabet) : Q £ ! Q ´ transition function
q 2 Q
112233©
s 2
ababab
a/b
(q,s)
2©311©©
blockingstate
Example: Graph representation:
1 2
3
a
b
a a
• one node per state(except for blocking state ©)
• one directed edge (arrow)from q to (q, s) with label sfor each pair (q, s) for which (q, s) ©
automataM
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Deterministic finite automaton
Notation: Given set Astring ´ finite sequence of symbols
´ empty stringA* ´ set of all strings of symbols in set Ae.g., A = {a, b}
s = abbbbaab 2 A*
s[3] = b (3rd element)| s | = 8 (length of string)
1 2
3
a
b
a a
Definition: Given • initial state q1 2 Q• set of final states F ½ Q
M accepts a string s 2 * with length n | s | if there exists a sequence of states q 2 Q* with length | q | = n+1 (execution) such that
1. q[1] = q1 (starts at initial state)2. q[i+1] = (q[i], s[i] ) , i 2 {1,2,…,n} (follows arrows with correct label)3. q[n+1] 2 F (ends in set of final states)
Definition: language accepted by automaton M L(M) › { set of all strings accepted by M }
There is no concept of time–the whole string is accepted “instantaneously”
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Deterministic finite automaton
Definition: Given • initial state q1 2 Q• set of final states F ½ Q
M accepts a string s 2 * with length n | s | if there exists a sequence of states q 2 Q* with length | q | = n+1 (execution) such that
1. q[1] = q1 (starts at initial state)2. q[i+1] = (q[i], s[i] ) , i 2 {1,2,…,n} (follows arrows with correct label)3. q[n+1] = F (ends in set of final states)
Definition: language accepted by automaton M L(M) › { set of all strings accepted by M }
1 2
3
a
b
a a
Example:q1 1F {1}L(M) = {, ab, aaa, abab, abaaa, aaaab, … }
= ( (ab)* (aaa)* )*
Questions in formal language theory:Is there a finite automaton that accepts a given language?Do two given automata accept the same language?What is the smallest automaton that accepts a given language? etc.
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Differential equation
Rn ´ state spaceRm ´ input spacef : Rn £ Rm ! Rn´ vector field
ordinarydifferentialequation
with input
Definition: Given an input signal u : [0,1) ! Rm
A signal x : [0,1) ! Rn is a solution to (in the sense of Caratheodory) if
1. x is piecewise differentiable
2.
If x is a solution then
at any time t for which the derivative exists
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Differential equation (no inputs)
Rn ´ state spacef : Rn ! Rn ´ vector field
ordinarydifferentialequation
without input
Definition:A signal x : [0,1) ! Rn is a solution to (in the sense of Caratheodory) if
1. x is piecewise differentiable
2.
If x is a solution then
at any time t for which the derivative exists
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Hybrid Automaton(Example #2: Thermostat)
heater
room
x ´ mean temperature x · 73 ?
x ¸ 77 ?
off mode on mode
Example: Q › { off, on } n › 1
Q ´ set of discrete states Rn ´ continuous state-spacef : Q £ Rn ! Rn ´ vector field : Q £ Rn ! Q ´ discrete transition
note “closed” inequalities associated with jump and “open” inequalities with flow
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Hybrid Automaton(Example #2: Thermostat)
mode q1 mode q3
(q1,x) = q3 ?
mode q2
(q1,x) = q2 ?
resets?
heater
room
x ´ mean temperature x · 73 ?
x ¸ 77 ?
off mode on mode
Q ´ set of discrete states Rn ´ continuous state-spacef : Q £ Rn ! Rn ´ vector field : Q £ Rn ! Q ´ discrete transition
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Hybrid Automaton
Q ´ set of discrete states Rn ´ continuous state-spacef : Q £ Rn ! Rn ´ vector field : Q £ Rn ! Q ´ discrete transition : Q £ Rn ! Rn ´ reset map
mode q1 mode q3
( q1,x–) = q3 ?
mode q2
(q1,x–) = q2 ?x(q1,x–)
x(q1,x–)
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Hybrid Automaton
Q ´ set of discrete states Rn ´ continuous state-spacef : Q £ Rn ! Rn ´ vector field : Q £ Rn ! Q £ Rn ´ discrete transition (& reset map)
mode q1 mode q3
1(q1,x–) = q3 ?
mode q2
1(q1,x–) = q2 ?x2(q1,x–)
x2(q1,x–)
Compact representation of a hybrid automaton
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Example #5: Multiple-tank system
y
pump goal ´ prevent the tank from emptying or filling up
constant outflow ´
pump-on inflow ´ ´ delay between command is sent to pump and the time it is executed
pump off
wait to on pump on
wait to off
y · ymin ?
y ¸ ymax ? › 0
¸ ?
¸ ?
› 0
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Example #5: Multiple-tank system
pump off
wait to on pump on
wait to off
y · ymin ?
y ¸ ymax ? › 0
¸ ?
¸ ?
› 0
Q › { off, won, on, woff }R2 ´ continuous state-space
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Definition: A solution to the hybrid automaton is a pair of right-continuous signals x : [0,1) ! Rn q : [0,1) ! Q
such that
1. x is piecewise differentiable & q is piecewise constant
2. on any interval (t1,t2) on which q is constant and x continuous
3.
Solution to a hybrid automaton
mode q1mode q2
1(q1,x–) = q2 ?x2(q1,x–)
continuous evolution
discrete transitions
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Hybrid Automaton(Example #2: Thermostat)
heater
room
x ´ mean temperature x · 73 ?
x ¸ 77 ?
off mode on mode
note “closed” inequalities associated with jumps and “open” inequalities with flows
73
77
q = on
x
off off offon on on
x – = 77, q – = on ) q = off no transition would occur if the “jump branch” had a strict inequality x > 77
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Example #7: Server system with congestion control
r
server
B
rate of service(bandwidth)
incoming rate
q
qmax
Additive increase/multiplicative decrease congestion control (AIMD):
• while q < qmax increase r linearly• when q reaches qmax instantaneously
multiply r by 2 (0,1)
q ¸ qmax ?
r r –
q(t)
t
queue dynamics
congestion controller
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Open Automaton(Example #7: Server system with congestion control)
r
server
B
rate of service(bandwidth)
incoming rate
q
qmax
q ¸ qmax ?
r r –
queue dynamics
congestion controller
congestion controller
queue dynamics
variabler
eventq ¸ qmax
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Open Automaton(Example #7: Server system with congestion control)
r
server
B
rate of service(bandwidth)
incoming rate
q
qmax
q ¸ qmax ?
r r –
queue-full
queue-full ?
eventqueue-full
variabler
queue dynamics
congestion controller
synchronized transitions(all guards m
ust hold for transition to occur)
events are nothing more than symbolic labels for transitions
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Open Automaton(Example #7: Server system with congestion control)
r
server
B
rate of service(bandwidth)
incoming rate
q
qmax
q ¸ qmax ?
r r –
queue-full
queue-full
queue dynamics
congestion controller
events are nothing more than symbolic labels for transitions
r
synchronized transitions(all guards m
ust hold for transition to occur)
eventqueue-full
variabler
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Open Automaton(Example #5: Multiple-tank system)
y
pump goal ´ prevent the tank from emptying or filling up
constant outflow ´
pump-on inflow ´ ´ delay between command is sent to pump and the time it is executed
pump off
wait to on pump on
wait to off
y · ymin ?
y ¸ ymax ? › 0
¸ ?
¸ ?
› 0
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Open Automaton(Example #5: Multiple-tank system)
pump off
pump on
change ?
y ¸ ymax ? ¸ ?
› 0
y · ymin ?
change ?
ask
ask
idle
delay
ask ?change
eventchange
eventask
y
pump goal ´ prevent the tank from emptying or filling up
constant outflow ´
pump-on inflow ´ ´ delay between command is sent to pump and the time it is executed
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Deterministic finite automaton
Q {q1, q2, …, qn} ´ finite set of states {a, b, c,… } ´ finite set of input symbols (alphabet) : Q £ ! Q ´ transition function
q 2 Q
112233©
s 2
ababab
a/b
(q,s)
2©311©©
blockingstate
Example: Graph representation:
1 2
3
a
b
a a
• one node per state(except for blocking state ©)
• one directed edge (arrow) from q to (q, s) with label sfor each pair (q, s) for which (q, s) ©
automataM
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Nondeterministic finite automaton
Q {q1, q2, …, qn} ´ finite set of states {a, b, c,… } ´ finite set of input symbols (alphabet) : Q £ ! 2Q ´ transition set-valued function
q 2 Q
112233©
s 2
ababab
a/b
(q,s)
{2}{©}{©}{1,3}{1}
{©} {©}
blockingstate
Example: Graph representation:
1 2
3
a
b
a b
automataM
Notation: Given a set A,2A ´ power-set of A, i.e., the set of all subsets of Ae.g., A = {1,2} ) 2A = {, {1}, {2}, {1,2} }When A has n < 1 elements then 2A has 2n elements
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Nondeterministic finite automaton
Definition: Given • initial state q1 2 Q• set of final states F ½ Q
M accepts a string s 2 * with length n | s | if there exists a sequence of states q 2 Q* with length | q | = n+1 (execution) such that
1. q[1] = q1 (starts at initial state)2. q[i+1] 2 (q[i], s[i] ) , i 2 {1,2,…,n} (follows arrows with correct label)3. q[n+1] 2 F (ends in set of final states)
Definition: language accepted by automaton M L(M) › { set of all strings accepted by M }
1 2
3
a
b
a b
Example:q1 1F {1}L(M) = { 2, ab, aba, abab, ababa, abaab, … }
= ( (ab)* (aba)* )*
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Determinization
1 2
3
a
b
a b
nondeterministic automaton M
1 2
1 or 3
a
b
a
b
deterministic automaton N
1 or 2
a
Same language:L(M) = L(N) = ( (ab)* (aba)* )*M provides more compact representation
From formal language theory:
For every nondeterministic finite automaton there is a deterministic one that accepts the same language (but generally the deterministic one needs more states)
• from 1 only accepts a and goes to 2• from 2 only accepts b and can go to either
1 or 3• from 1 or 3 only accepts a and goes to 2 or
1 resp.• from 1 (or 2) can accepts a and go to 2
from (1 or) 2 can accept b and go to 1 or 3
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Example #3: Transmission
throttleu 2 [-1,1]
g 2 {1,2,3,4}gear
position
velocity
k ´ efficiency of the kth gear
velocity
1
2 3
4
[Hedlund, Rantzer 1999]
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Example #3: Semi-automatic transmission
g = 1 g = 2 g = 3 g = 4
v(t) 2 { up, down, keep } ´ drivers input (discrete)
v = up or ¸ 2 ? v = up or ¸ 3 ? v = up or ¸ 4 ?
v = down or · 1 ? v = down or · 2 ? v = down or · 3 ?
2
3
4
1
2
3
g = 1g = 2
g = 3
g = 4
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Nondeterministic Hybrid Automaton(Example #3: Semi-automatic transmission)
g = 1 g = 2 g = 3 g = 4
¸ 1 ? ¸ 2 ? ¸ 3 ?
· 2 ? · 3 ? · 4 ?
2
3
4
1
2
3
g = 1g = 2
g = 3
g = 4
Suppose we want to consider all possible driver inputs:
· 22· · 4 ¸ 3
1· · 3
guard condition(does not force jump,
simply allows it)
invariance condition(must hold to remain
in discrete state)
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Nondeterministic Hybrid Automaton
Q ´ set of discrete states Rn ´ continuous state-spacef : Q £ Rn ! Rn ´ vector field : Q £ Rn ! 2Q ´ set-valued discrete transition : Q £ Rn ! 2Rn ´ set-valued reset map½ Q £ Rn ´ domain or invariant set
mode q1 mode q3
q3 2(q1,x–) ?
mode q2
q2 2(q1,x–) ?x2(q1,x–)
x2(q1,x–)
(q1,x) 2 (q3,x) 2 (q2,x) 2
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Nondeterministic Hybrid Automaton
Q ´ set of discrete states Rn ´ continuous state-spacef : Q £ Rn ! Rn ´ vector field: Q £ Rn ! 2Q£Rn ´ set-valued discrete transition (& reset & domain)
mode q1 mode q3mode q2
(q2, x) 2(q1,x–)
(q1,x) 2 qx
Compact representation of a nondeterministic hybrid automaton
(q3,x) 2 qx(q2,x) 2 qx
(q3, x) 2(q1,x–)
? ›
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Nondeterministic Hybrid Automaton(Example #3: Semi-automatic transmission)
g = 1 g = 2
¸ 1 ?
· 2 ?
2
1
g = 1
g = 2
· 21· · 3
guard condition(does not force jump,
simply allows it)
invariance condition(must hold to remain
in discrete state)
Q › { 1, 2 }R2 ´ continuous state-space
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Solution to a nondeterministic hybrid automaton
Definition: A solution to the hybrid automaton is a pair of right-continuous signals x : [0,1) ! Rn q : [0,1) ! Q
such that
1. x is piecewise differentiable & q is piecewise constant
2. on any interval (t1,t2) on which q is constant and x continuous
3.
mode q1mode q2
continuous evolution
discrete transition & resets & domain
(q2, x) 2(q1,x–)
(q1,x) 2 qx(q2,x) 2 qx
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Stochastic finite automaton: controlled Markov chaina
1 2
3
20% | b
a 80% | b
Q {q1, q2, …, qn} ´ finite set of states {a, b, c,… } ´ finite set of input symbols : Q £ Q £ ! [0,1] ´ transition probability function
controlled Markov chain M
(q1, q2, s ) ´ probability of transitioning to state q2, when in state q1 and symbol s is selected
By convention, typically• edges drawn without probabilities correspond to
transitions that occur with probability 1• self-loops may be omitted
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Stochastic finite automaton: controlled Markov chain
100% | a
1 2
3
20% | b80% | b
Q › {1, 2, 3} › {a, b}(q1, q2, s ) ´ probability of transitioning to state q2, when in
state q1 and symbol s is selected
By convention, typically• edges drawn without probabilities
correspond to transitions that occur with probability 1
• self loops may be omitted
100% | a
100% | a
100% | b
100% | b
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Stochastic Hybrid Automaton
Q ´ set of discrete states Rn ´ continuous state-spacef : Q £ Rn ! Rn ´ vector field: Q £ Q £ Rn ! [0,1] ´ discrete transition probability: Q £ Q £ Rn ! Rn ´ reset map (deterministic)
mode q1 mode q3mode q2
(q1, q2, x–)x(q1, q2, x–)
x(q1, q3, x–)(q1, q3, x–)
(Poisson-like model)
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Stochastic Hybrid Automaton
Q ´ set of discrete states Rn ´ continuous state-spacef : Q £ Rn ! Rn ´ vector field: Q £ Q £ Rn £ Rn ! [0,1] ´ discrete transition probability & reset
mode q1 mode q3mode q2
(q1, q2, x–, x)
(q1, q3, x–, x)
(Poisson-like model)
More as special topic later…
![Page 38: Lecture #2 How to describe a hybrid system? Formal models for hybrid system João P. Hespanha University of California at Santa Barbara Hybrid Control and.](https://reader035.fdocuments.us/reader035/viewer/2022062322/5697c00d1a28abf838cc9821/html5/thumbnails/38.jpg)
Next class…
1. Trajectories of hybrid systems:• Solution to a hybrid system• Execution of a hybrid system
2. Degeneracies• Finite escape time• Chattering• Zeno trajectories• Non-continuous dependency on initial conditions